Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation

Huang, Chao; Wang, Zhenhua; Zhao, Zihan; Wang, Long; Lai, Chun Sing; Wang, Dong

doi:10.1109/access.2018.2833858

Cited by 107 publications

(50 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different variants of the KF, such as the extended Kalman filter (EKF), have been used to deal with nonlinearities of the system [54]. Recently, the EKF has attracted substantial interest as a method to measure the SOC [78]. In the EKF, nonlinear system dynamics and model measurements have been expanded through a linearization method, which linearize the battery model at each time step.…”

Section: Reference Mae (%)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

View full text Add to dashboard Cite

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

show abstract

Section: Reference Mae (%)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…However, it is impossible to directly measure the SOC by placing sensors inside the battery because the battery itself is a closed system. Moreover, many factors such as charging/discharging current rate, battery aging, and ambient temperature will affect the SOC estimation accuracy . Therefore, the accurate and robust estimation of battery SOC is still a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, many factors such as charging/discharging current rate, battery aging, and ambient temperature will affect the SOC estimation accuracy. 3 Therefore, the accurate and robust estimation of battery SOC is still a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

2020

View full text Add to dashboard Cite

Summary This study simultaneously considers the state‐of‐charge (SOC) estimation and model parameter identification of lithium‐ion batteries with outliers in measurements. Conventional Kalman‐type filters may degrade performance in this case since they assume Gaussian‐distributed measurement noise. To improve the SOC estimation accuracy under this condition, a robust normal‐gamma (NG)‐based adaptive dual unscented Kalman filter (NG‐ADUKF) is proposed. First, by modeling the joint distribution of the state and auxiliary variables of the measurement noise as the NG distribution, the unscented Kalman filter (UKF) is integrated with the NG filter to deal with the heavy‐tailed measurement noise. Second, the online parameter identification and SOC estimation are realized simultaneously by alternatively using two NG‐based adaptive UKFs. The performance of the proposed algorithm is validated by the New European Driving Cycle and Urban Dynamometer Driving Schedule tests. Experimental results show that the proposed NG‐ADUKF algorithm has more accurate SOC estimations compared with the dual UKF (DUKF) and the variational Bayes‐based adaptive DUKF (VB‐ADUKF) in the case of mistuning and outliers. Moreover, the proposed method is more computationally efficient than VB‐ADUKF.

show abstract

“…3 As a result, it is quite difficult to obtain accurate working state descriptions of the lithium-ion battery packs using the current power supply conditions. 5 The existing research has carried out a great work by improving the safety level of the lithium-ion battery pack effectively. 5 The existing research has carried out a great work by improving the safety level of the lithium-ion battery pack effectively.…”

Section: Introductionmentioning

confidence: 99%

“…4 The existing modelling methods reported in literature so far cannot resolve the life shortening, thermal runaway and other issues in the lithium-ion battery packs for reliable power supply purposes. 5 The existing research has carried out a great work by improving the safety level of the lithium-ion battery pack effectively.…”

Section: Introductionmentioning

confidence: 99%

An improved packing equivalent circuit modeling method with the cell‐to‐cell consistency state evaluation of the internal connected lithium‐ion batteries

Wang

Shi

Fernandez

et al. 2019

Energy Science & Engineering

View full text Add to dashboard Cite

The existing equivalent modeling methods reported in literature focuses mainly on the battery cells and do not take the packing consistency state into consideration, which exists on the internal connected cells of the lithium‐ion battery pack. An improved equivalent circuit model is constructed and reported in this manuscript for the first time, which can be used to characterize the working characteristics of the packing lithium‐ion batteries. A new equilibrium concept named as state of balance is proposed as well as the calculation process, which is realized by considering the real‐time detected internal battery cell voltages. In addition, this new equilibrium concept aims to obtain more information on the real‐time consistency characterization of the battery pack. The improved adaptive equivalent circuit model is investigated by using the improved splice modeling method, in which the statistical noise properties are corrected and the additional parallel resistance‐capacitance circuit is introduced. The parameter correction treatment is carried out by comparing the estimated and experimental detected closed circuit voltages. Furthermore, the tracking error is found to be 0.005 V and accounts for 0.119% of the nominal battery voltage. By taking the packing consistency state and temperature correction into consideration, the accurate working characteristic expression is realized in the improved equivalent circuit modeling process. Finally, the model proposed in this manuscript presents a great number of advantages compared to other methods reported so far, like has the high accuracy, and the ability to protect the security of the lithium‐ion battery pack in the power supply application.

show abstract

Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation

Cited by 107 publications

References 39 publications

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

An improved packing equivalent circuit modeling method with the cell‐to‐cell consistency state evaluation of the internal connected lithium‐ion batteries

Contact Info

Product

Resources

About